軟體更新日誌V3.0B版本，在2.0基礎上實現了呼叫攝像頭的實時識別功能。V3.0A版若不實時重新整理識別畫面，可以手動點一次識別一次……

附上完整程式碼，也是做個程式碼備份。需要的小夥伴自取就ok～

軟體目前存在的bug是1.在開始識別後會很卡，目前認為可能的原因是每次重新整理都重複載入了模型檔案；2.攝像頭畫面與實時識別畫面比例不一樣，後期優化一下。

# coding:utf-8
'''
V3.0B版本，實現攝像頭識別,實時重新整理顯示結果
@author:kangshifu
'''
import numpy as np
import cv2
import os
import os.path
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import pylab
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *

class UiForm():

    def setupUi(self, Form):
        Form.setObjectName("Form")
        Form.resize(600, 420)
        Form.setMinimumSize(QtCore.QSize(600, 420))
        Form.setMaximumSize(QtCore.QSize(600, 420))
        self.frame = QtWidgets.QFrame(Form)
        self.frame.setGeometry(QtCore.QRect(20, 20, 550, 100))
        self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
        self.frame.setFrameShadow(QtWidgets.QFrame.Raised)
        self.frame.setObjectName("frame")
        self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.frame)
        self.horizontalLayout_2.setObjectName("horizontalLayout_2")
        # 載入模型檔案按鈕
        self.btn_add_file = QtWidgets.QPushButton(self.frame)
        self.btn_add_file.setObjectName("btn_add_file")
        self.horizontalLayout_2.addWidget(self.btn_add_file)
        # 載入pbtxt檔案按鈕
        self.btn_add_pbtxt = QtWidgets.QPushButton(self.frame)
        self.btn_add_pbtxt.setObjectName("btn_add_pbtxt")
        self.horizontalLayout_2.addWidget(self.btn_add_pbtxt)
        # 輸入檢測類別數目按鈕
        self.btn_enter = QtWidgets.QPushButton(self.frame)
        self.btn_enter.setObjectName("btn_enter")
        self.horizontalLayout_2.addWidget(self.btn_enter)
        # 開啟攝像頭
        self.btn_opencam = QtWidgets.QPushButton(self.frame)
        self.btn_opencam.setObjectName("btn_objdec")
        self.horizontalLayout_2.addWidget(self.btn_opencam)
        # 開始識別按鈕
        self.btn_objdec = QtWidgets.QPushButton(self.frame)
        self.btn_objdec.setObjectName("btn_objdec")
        self.horizontalLayout_2.addWidget(self.btn_objdec)
        # 退出按鈕
        self.btn_exit = QtWidgets.QPushButton(self.frame)
        self.btn_exit.setObjectName("btn_exit")
        self.horizontalLayout_2.addWidget(self.btn_exit)
        # 顯示攝像頭的畫面
        self.lab_rawimg_show = QtWidgets.QLabel(Form)
        self.lab_rawimg_show.setGeometry(QtCore.QRect(20, 140, 260, 260))
        self.lab_rawimg_show.setMinimumSize(QtCore.QSize(260, 260))
        self.lab_rawimg_show.setMaximumSize(QtCore.QSize(260, 260))
        self.lab_rawimg_show.setObjectName("lab_rawimg_show")
        self.lab_rawimg_show.setStyleSheet(("border:2px solid red"))

        # 顯示攝像頭的畫面
        self.lab_decimg_show = QtWidgets.QLabel(Form)
        self.lab_decimg_show.setGeometry(QtCore.QRect(300, 140, 260, 260))
        self.lab_decimg_show.setMinimumSize(QtCore.QSize(260, 260))
        self.lab_decimg_show.setMaximumSize(QtCore.QSize(260, 260))
        self.lab_decimg_show.setObjectName("lab_decimgimg_show")
        self.lab_decimg_show.setStyleSheet(("border:2px solid green"))
        
        self.retranslateUi(Form)
        # 這裡將按鈕和定義的動作相連，通過click訊號連線openfile槽？
        self.btn_add_file.clicked.connect(self.openpb)
        # 用於開啟pbtxt檔案
        self.btn_add_pbtxt.clicked.connect(self.openpbtxt)
        # 用於使用者輸入類別數
        self.btn_enter.clicked.connect(self.enter_num_cls)
        # 開啟攝像頭
        self.btn_opencam.clicked.connect(self.opencam)
        # 開始識別
        self.btn_objdec.clicked.connect(self.refresh_objdec)
        # 這裡是將btn_exit按鈕和Form視窗相連，點選按鈕傳送關閉視窗命令
        self.btn_exit.clicked.connect(Form.close)
        QtCore.QMetaObject.connectSlotsByName(Form)

    def retranslateUi(self, Form):
        _translate = QtCore.QCoreApplication.translate
        Form.setWindowTitle(_translate("Form", "目標檢測"))
        self.btn_add_file.setText(_translate("Form", "載入模型檔案"))
        self.btn_add_pbtxt.setText(_translate("Form", "載入pbtxt檔案"))
        self.btn_enter.setText(_translate("From", "指定識別類別數"))
        self.btn_opencam.setText(_translate("Form", "開啟攝像頭"))
        self.btn_objdec.setText(_translate("From", "開始識別"))
        self.btn_exit.setText(_translate("Form", "退出"))
        self.lab_rawimg_show.setText(_translate("Form", "攝像頭原始畫面"))
        self.lab_decimg_show.setText(_translate("Form", "實時識別效果"))
        
    def openpb(self):
        global openfile_name_pb
        openfile_name_pb, _ = QFileDialog.getOpenFileName(self.btn_add_file,'選擇pb檔案','/home/kanghao/','pb_files(*.pb)')
        print('載入模型檔案地址為：' + str(openfile_name_pb))
        
    def openpbtxt(self):
        global openfile_name_pbtxt
        openfile_name_pbtxt, _ = QFileDialog.getOpenFileName(self.btn_add_pbtxt,'選擇pbtxt檔案','/home/kanghao/','pbtxt_files(*.pbtxt)')
        print('載入標籤檔案地址為：' + str(openfile_name_pbtxt))
    
    def opencam(self):
        self.camcapture = cv2.VideoCapture(0)
        self.timer = QtCore.QTimer()
        self.timer.start()
        self.timer.setInterval(3) # 0.1s重新整理一次
        self.timer.timeout.connect(self.camshow)
        
    def camshow(self):
        global camimg
        _ , camimg = self.camcapture.read()
        # ~ print(_)
        #camimg = cv2.resize(camimg, (512, 512))
        camimg = cv2.cvtColor(camimg, cv2.COLOR_BGR2RGB)
        # ~ print(type(camimg))
        #strcamimg = camimg.tostring()
        showImage = QtGui.QImage(camimg.data, camimg.shape[1], camimg.shape[0], QtGui.QImage.Format_RGB888)
        self.lab_rawimg_show.setPixmap(QtGui.QPixmap.fromImage(showImage))
        
    def enter_num_cls(self):
        global num_class
        num_class, okPressed = QInputDialog.getInt(self.btn_enter,'指定訓練類別數','你的目標有多少類？',1,1,28,1)
        if okPressed:
            print('識別目標總類為：' + str(num_class))

    def img2pixmap(self, image):
        Y, X = image.shape[:2]
        self._bgra = np.zeros((Y, X, 4), dtype=np.uint8, order='C')
        self._bgra[..., 0] = image[..., 2]
        self._bgra[..., 1] = image[..., 1]
        self._bgra[..., 2] = image[..., 0]
        qimage = QtGui.QImage(self._bgra.data, X, Y, QtGui.QImage.Format_RGB32)
        pixmap = QtGui.QPixmap.fromImage(qimage)
        return pixmap

    def refresh_objdec(self):
        self.timerdec = QtCore.QTimer()
        self.timerdec.start()
        self.timerdec.setInterval(0.3) # 0.1s重新整理一次
        self.timerdec.timeout.connect(self.object_detection)

    def object_detection(self):
        sys.path.append("..")
        from object_detection.utils import ops as utils_ops

        if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
            raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')
        
        from utils import label_map_util

        from utils import visualization_utils as vis_util

        # Path to frozen detection graph. This is the actual model that is used for the object detection.
        PATH_TO_FROZEN_GRAPH = openfile_name_pb

        # List of the strings that is used to add correct label for each box.
        PATH_TO_LABELS = openfile_name_pbtxt

        NUM_CLASSES = num_class

        detection_graph = tf.Graph()
        with detection_graph.as_default():
          od_graph_def = tf.GraphDef()
          with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')
            
        category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

            
        # For the sake of simplicity we will use only 2 images:
        # image1.jpg
        # image2.jpg
        # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
        TEST_IMAGE_PATHS = camimg
        print(TEST_IMAGE_PATHS)
        # Size, in inches, of the output images.
        IMAGE_SIZE = (12, 8)

        def run_inference_for_single_image(image, graph):
          with graph.as_default():
            with tf.Session() as sess:
              # Get handles to input and output tensors
              ops = tf.get_default_graph().get_operations()
              all_tensor_names = {output.name for op in ops for output in op.outputs}
              tensor_dict = {}
              for key in [
                  'num_detections', 'detection_boxes', 'detection_scores',
                  'detection_classes', 'detection_masks'
              ]:
                tensor_name = key + ':0'
                if tensor_name in all_tensor_names:
                  tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
                      tensor_name)
              if 'detection_masks' in tensor_dict:
                # The following processing is only for single image
                detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
                detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
                # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
                real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
                detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                    detection_masks, detection_boxes, image.shape[0], image.shape[1])
                detection_masks_reframed = tf.cast(
                    tf.greater(detection_masks_reframed, 0.5), tf.uint8)
                # Follow the convention by adding back the batch dimension
                tensor_dict['detection_masks'] = tf.expand_dims(
                    detection_masks_reframed, 0)
              image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

              # Run inference
              output_dict = sess.run(tensor_dict,
                                     feed_dict={image_tensor: np.expand_dims(image, 0)})

              # all outputs are float32 numpy arrays, so convert types as appropriate
              output_dict['num_detections'] = int(output_dict['num_detections'][0])
              output_dict['detection_classes'] = output_dict[
                  'detection_classes'][0].astype(np.uint8)
              output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
              output_dict['detection_scores'] = output_dict['detection_scores'][0]
              if 'detection_masks' in output_dict:
                output_dict['detection_masks'] = output_dict['detection_masks'][0]
          return output_dict
          

        #image = Image.open(TEST_IMAGE_PATHS)
        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.
        #image_np = load_image_into_numpy_array(TEST_IMAGE_PATHS)
        image_np = TEST_IMAGE_PATHS
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        # Actual detection.
        output_dict = run_inference_for_single_image(image_np, detection_graph)
        # Visualization of the results of a detection.
        vis_util.visualize_boxes_and_labels_on_image_array(
            image_np,
            output_dict['detection_boxes'],
            output_dict['detection_classes'],
            output_dict['detection_scores'],
            category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True,
            line_thickness=8)
        #plt.figure(figsize=IMAGE_SIZE)
        #plt.imshow(image_np)
        #plt.savefig(str(TEST_IMAGE_PATHS)+".jpg")
        self.lab_decimg_show.setPixmap(self.img2pixmap(image_np))
        self.lab_decimg_show.setScaledContents(True)

## 用於顯示ui介面的命令
if __name__ == "__main__":
    app = QtWidgets.QApplication(sys.argv)
    Window = QtWidgets.QWidget()
    # ui為根據類Ui_From()建立的例項
    ui = UiForm()
    ui.setupUi(Window)
    Window.show()
    sys.exit(app.exec_())